A unifying representation for a class of dependent random measures

We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of a specific nonparametric model, but can be applied to all models that can be represented using completely random measures. Several existing dependent random measures can be seen as specific cases of this framework. Interesting properties of the resulting measures are derived and the efficacy of the framework is demonstrated by constructing a covariate-dependent latent feature model and topic model that obtain superior predictive performance

Comparison of Rocket Performance using Exhaust Diffuser and Conventional Techniques for Altitude Simulation

A rocket engine with an exhaust-nozzle area ratio of 25 was operated at a constant chamber pressure of 600 pounds per square inch absolute over a range of oxidant-fuel ratios at an altitude pressure corresponding to approximately 47,000 feet. At this condition, the nozzle flow is slightly underexpanded as it leaves the nozzle. The altitude simulation was obtained first through the use of an exhaust diffuser coupled with the rocket engine and secondly, in an altitude test chamber where separate exhauster equipment provided the altitude pressure. A comparison of performance data from these two tests has established that a diffuser used with a rocket engine operating at near-design nozzle pressure ratio can be a valid means of obtaining altitude performance data for rocket engines

Generalising Deep Learning MRI Reconstruction across Different Domains

We look into robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs. We then propose to generalise the network by training with large publicly-available natural image datasets with synthesised phase information to achieve high cross-domain reconstruction performance which is competitive with domain-specific training. To explain its generalisation mechanism, we have also analysed patch sets for different training datasets.Comment: Accepted for ISBI2019 as a 1-page abstrac

The Impact of Systems Thinking as a Construct of Organizational Learning on Competitive Advantage in Kenya’s Oil Marketing Sector

Introduction: Systems thinking has emerged as the convergence point between sciences, a fundamental way of interpreting nature and mastering the ever increasing complexity of the products of human intelligence. Objective: This study aimed to determine the impact of systems thinking as a construct of organizational learning on competitive advantage in Kenya’s Oil Marketing Sector. The latent aspects of competitive advantage; organization agility, innovation, barriers to entry, mass customization and inimitability (difficulty to duplicate) were investigated against the independent variable. Methodology: The research design was explanatory, non-contrived and cross-sectional study on Kenya’s oil marketing sector. A sample size of 425 was drawn from oil marketing companies that had a market share above 1% according to the Petroleum Institute of East Africa. Structured questionnaires were used as the data collection tool. Correlation, regression and SEM model were used to analyze the study findings. Findings: The study found that systems thinking significantly predicted competitive advantage which indicated rejection of the null hypothesis. Keywords: Organizational Learning, Systems Thinking, Competitive Advantage, Oil Marketing Sector

The Impact of Mental Models as a Construct of Organizational Learning on Competitive Advantage in Kenya’s Oil Marketing Sector

Introduction: Despite the growing popularity of organizational learning (OL) and its constructs, the concept remains complex and vague for researchers as well as managers. Mental models are inherently difficult to study and several methods have been developed that essentially document a mental model in the form of a mind map or concept diagram. Objective: This study aimed to determine the impact of mental models as a construct of organizational learning on competitive advantage in Kenya’s Oil Marketing Sector. The latent aspects of competitive advantage; organization agility, innovation, barriers to entry, mass customization and inimitability (difficulty to duplicate) were investigated against the independent variable. Methodology: The research design was explanatory, non-contrived and cross-sectional study on Kenya’s oil marketing sector. A sample size of 425 was drawn from oil marketing companies that had a market share above 1% according to the Petroleum Institute of East Africa. Structured questionnaires were used as the data collection tool. Correlation, regression and SEM model were used to analyze the study findings. Findings: The study found that mental models significantly predicted competitive advantage which indicated rejection of the null hypothesis. Keywords: Organizational Learning, Mental Models, Competitive Advantage, Oil Marketing Sector